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..
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figures
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images
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solutions
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01 Introduction to Machine Learning.ipynb
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02 Scientific Computing Tools in Python.ipynb
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03 Data Representation for Machine Learning.ipynb
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04 Training and Testing Data.ipynb
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05 Supervised Learning - Classification.ipynb
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06 Supervised Learning - Regression.ipynb
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07 Unsupervised Learning - Transformations and Dimensionality Reduction.ipynb
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08 Unsupervised Learning - Preprocessing.ipynb
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10 Unsupervised Learning - Clustering.ipynb
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11 Review of Scikit-learn API.ipynb
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12 Case Study - Titanic Survival.ipynb
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13 Text Feature Extraction.ipynb
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14 Case Study - SMS Spam Detection.ipynb
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15 Cross Validation.ipynb
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18 Model Complexity and GridSearchCV.ipynb
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19 Pipelining Estimators.ipynb
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20 Performance metrics and Model Evaluation.ipynb
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21 In Depth - Linear Models.ipynb
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22 In Depth - Support Vector Machines.ipynb
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23 In Depth - Trees and Forests.ipynb
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24 Feature Selection.ipynb
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25 Unsupervised learning - Hierarchical and density-based clustering algorithms.ipynb
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26 Unsupervised learning - Non-linear dimensionality reduction.ipynb
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old-03.1 Case Study - Supervised Classification of Handwritten Digits.ipynb
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old-03.3 Case Study - Face Recognition with Eigenfaces.ipynb
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old-04.3 Analyzing Model Capacity.ipynb
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old-07.1 Case Study - Large Scale Text Classification.ipynb
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helpers.py
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